Various classification problems exist. One such problem is people counting. Configuring a computing system to accurately identify a number of people in an environment based on a visual representation of the environment, such as an image, is a challenging task. The difficulty is exacerbated when the environment is crowded and occluded. Conventional methods of computer-based people counting are limited and tend to be slow, inaccurate, or both.
One conventional method of people counting relies upon measuring input and output flows to indirectly infer the number of people. For example, turnstiles can be used to measure the number of people that enter a space and the number of people that exit the space. By summing the measured number of people that enter the space and subtracting the measured number of people that exit the space, a calculated number of people is arrived at. However, the calculated number is really just an inference and depends upon several unverifiable assumptions, such as accurate measurement (e.g., no turnstile jumpers) and measurement at all egress and ingress points (no unmonitored ingress or egress points). The difference between the number of people measured and the actual number of people (the error) is typically undesirably high with conventional systems.